This work pursues an alternate approach using deep learning to remove the effect of motion-dependent shear from the sheared tactile image, offering several advantages that were summarized in the introduction. Our approach to disentangling environmental factors of variation and object attributes in tactile sensing is based on work in computer vision where disentanglement is considered important to improve the generalization of machine learning models, for example to aid in learning of robust object representations for object classification under novel scenarios [1]}. Once disentangled, different environmental factors and object attributes can be recombined to generate coherent novel concepts thus extending knowledge to previously unobserved scenarios [1]}. For example, synthesizing sensor responses to novel stimuli by combining the stimuli attributes and factor of variation without actually observing them to plan action under previously unobserved conditions. In the case of tactile sensing considered here, the stimuli attributes could be novel contact geometry and the factors of variation the motion-induced shear.
